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Extract, Retrieve and Predict kcat values for a metabolic model to run enzyme constrained metabolic pipelines.

Project description

WILDkCAT

WILDkCAT is a set of scripts designed to extract, retrieve, and predict enzyme turnover numbers (kcat) for genome-scale metabolic models.


Installation

  1. Clone the repository:
git clone https://github.com/h-escoffier/WILDkCAT.git
cd WILDkCAT
  1. Install Miniconda or Anaconda.

  2. Create and activate the environment:

conda env create -f environment.yml
conda activate wildkcat-env
  1. Provide your BRENDA login credentials and Entrez API email adress to query the BRENDA enzyme database and NCBI database.

Create a file named .env in the root of your project with the following content:

ENTREZ_EMAIL=your_registered_email@example.com
BRENDA_EMAIL=your_registered_email@example.com
BRENDA_PASSWORD=your_password
  • Replace the placeholders with the credentials from the account you created on the BRENDA website.
  • Make sure this file is not shared publicly (e.g., add .env to your .gitignore) since it contains sensitive information.
  • The scripts will automatically read these environment variables to authenticate and retrieve kcat values.

Scripts Overview

extract_kcat.py

  • Verifies whether the reaction EC number exists.
  • Retains inputs where reaction-associated genes/enzymes are not supported by KEGG.
  • Retains inputs where no enzymes are provided by the model.

retrieve_kcat.py

  • If multiple enzymes are provided, searches UniProt for catalytic activity.
  • If multiple catalytic enzymes are identified, store all.
  • When multiple enzymes are found, computes identity percentages relative to the identified catalytic enzyme.
  • Applies Arrhenius correction to values within the appropriate pH range.
  • For rows with multiple scores, selects:
    • The best score
    • The highest identity percentage
    • The lowest kcat value

predict_kcat.py

  • If multiple enzymes are provided, searches UniProt for catalytic activity.
  • Skips entries missing KEGG compound IDs.

TODO

  • generate_reports.py: Optimize the functions from generate_reports.py to have html plots
  • catapro.py: Uniprot quieries can be send as batches
  • sabio_rk_api.py: Fix SABIO-RK
  • catapro.py: Move PubChem API queries to a dedicated module ?
  • predict_kcat.py : Integrate TurNuP kcat prediction
  • generate_reports.py - general_report(): Add the coverage of the reactions with kcat values in the model

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